Method and apparatus for assisted trajectory planning

ABSTRACT

A procedure can be assisted by a processor system, such as a computer system. A trajectory can be used to identify a selected trajectory or path of an instrument to reach a tumor within a brain of a subject, reach a selected portion of the anatomy (e.g. sub-thalamic nucleus (STN) or spinal cord), or other appropriate target. The planning algorithm can include both inputted data and learned rankings or ratings related to selected trajectories. The planning algorithm can used the learned ratings to rate and later determined trajectories.

FIELD

The subject disclosure relates to assisted surgical procedures, andparticularly to system training for assisting in or rating procedures.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

Procedures can be performed on various structures, such as a humananatomy or other animal anatomies. The procedures, however, maygenerally be either open procedures or closed or less invasiveprocedures. In an open procedure, the anatomy of the patient or subjectis open for viewing by a surgeon. In a less invasive procedure, however,it can be selected to minimize the access or viewing of the internalportions of the subject. It may be selected, therefore, to use imagingto assist in performing a less invasive procedure.

Images of the subject can be used to assist in performing a procedure byillustrating the internal structure of the subject. Various tracking andnavigation systems can be used to assist in locating and illustratingthe location of the instrument relative to the structure by displayingan icon relative to the image. For example, an icon representing aninstrument can be super-imposed on the image of the structure of thesubject to illustrate the location of the instrument relative to thesubject.

The instrument can be passed through the subject at various entrylocations, angles, and depths relative to the subject. These variousfeatures can be defined as a trajectory which includes an entry pointand a path of the instrument from the entry point to a selected target.The trajectory may also be defined by any appropriate geometric shape,such as cones, regular or irregular volume shapes, cylinders, etc. Asurgeon or other appropriate user can identify a trajectory to reach atarget that is to be followed during an actual procedure. Accordingly,image data of the subject can be acquired prior to performing aprocedure and the procedure can be planned with the image data to assistin defining an appropriate trajectory to the target.

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

An assisted procedure can be assisted by a computer system. The computersystem can include an appropriate processor (e.g. processing coresprovided by corporations such as Intel Corporation or Advanced MicroDevices) or processing system that can execute instructions based on atrained algorithm to assist in solving or identifying a problem. Forexample, a procedure or algorithm can be used to assist in planning atrajectory to perform a procedure. A trajectory can be used to identifya path of an instrument to reach a tumor within a brain of a subject,reach a selected portion of the anatomy (e.g. sub-thalamic nucleus (STN)or spinal cord), or other appropriate targets. The trained algorithm caninclude both inputted data and learned rankings or ratings related toselected trajectories to assist in determining and displaying atrajectory for a selected procedure. The trained algorithm can alsoinclude information or data related to previously performed surgeries,case studies from literature or provided by selected surgeons, orratings or rankings of trajectories deemed safe, efficacious, orappropriate from any appropriate source. For example, an assistedtrajectory planning (ATP) system can include both learned coefficientsfrom ratings based upon previously performed (also referred to astraining datasets) procedures. Online or onsite training can use lateracquired or clinical datasets of a user or clinician. Also, datasets caninclude current subject datasets which can be for a subject for which aprocedure is to be rated and/or performed. Using ratings of previouslyperformed procedures can be referred to as offline or prior training.The training based upon current clinician procedures ratings can bereferred to as online training. Regardless of the training, the systemcan use ratings of selected trajectories to assist in identifyingfeatures to determine coefficients to propose ratings of selectedtrajectories for use by a user. Additionally, the online training can beused to assist in personalizing or specifying ratings for a particularclinician (such as a surgeon) based upon the on-line or active training.Additionally, the online training can include sampling and/or resamplingdata from various performed procedures for learning coefficients to newtargets. For example, bootstrapping regression, jackknifing,cross-validation, randomization tests, and permutation tests can be usedto analyze and test data to determine coefficients for selected targetsnot previously within the algorithms range.

Accordingly, the ATP system can be used to provide a generic ratingsystem that can be augmented on site or online by a clinician to assistin minimizing variances between ratings of various trajectories givenbased on the ATP system and ratings that would be applied by theparticular clinician.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 is an overview of a planning algorithm and procedure;

FIG. 2 is an overview plan of a clinical application of the planningalgorithm;

FIG. 3 is a flowchart regarding offline learning;

FIG. 4 is an exemplary illustration of at least a portion of a dataset;

FIG. 5 is a diagram of a shell sample technique;

FIG. 6 is a flowchart of a use of a rating algorithm according tovarious embodiments of the subject disclosure;

FIG. 7 is a display of an output of the rating algorithm, according tovarious embodiments of the subject disclosure;

FIG. 8 is a flowchart of an online learning portion of the learningalgorithm, according to various embodiments; and

FIG. 9 is an environmental diagram of a planning and navigation systemfor assisting in planning and/or performing a procedure, according tovarious embodiments.

Corresponding reference numerals indicate corresponding parts throughoutthe several views of the drawings.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

The subject disclosure relates generally to performing a procedure on asubject, which can include a human subject. It will be understood,however, that the subject can include any appropriate subject where aprocedure can be planned to move an instrument from an exterior of thesubject into an interior of the subject. The subject can include variousanimate or inanimate objects. For example, it can be selected to move aninstrument from exterior to a shell or casing of an automotive orelectronic system without removing large portions to reach the internalportions. Images of the selected subject's system can be acquired andtrajectories can be planned to move an instrument from the exterior tothe interior of the subject to perform a function, such as repair orremoval of a selected component within the inanimate object.Accordingly, it will be understood that the subject disclosure is notlimited to performing a procedure on a human anatomy, but rather thatthe subject disclosure is related generally to a procedure, includingtraining a planning system, and performing a procedure with assistanceof the planning system or the assisted trajectory planning (ATP) systemon any appropriate subject.

With reference to FIG. 1, a general overview flowchart 10 of training anATP system and performing a procedure with an ATP system is illustrated.The overview flowchart 10 illustrated in FIG. 1 illustrates a very broadview of a system as herein discussed. Generally the flowchart 10illustrates and describes a generation of an ATP system and a usethereof. With reference to FIG. 2 an exemplary use of the broad system10 is illustrated. FIGS. 3, 6, and 8 illustrate in greater detailvarious portions of the system discussed in FIGS. 1 and 2.

With initial reference to FIG. 1, the ATP system and procedure accordingto the flow chart 10 can begin in START block 12. Once the procedure isstarted, the procedure can proceed into an assisted trajectory procedure(ATP) program 14. The ATP program 14 can include or be trained with bothan offline learning feature in block 16 and an online learning featurein block 18. The ATP program 14, however, need not include both offlinelearning 16 and online learning 18. As discussed herein, the offlinelearning portion in block 16 (exemplarily illustrated in flowchart 100in FIG. 3) can include a system or procedure that allows training theATP learning program 14 using prior acquired data (also referred to astraining datasets) and/or a plurality of inputs (also referred to astraining ratings) from a plurality of experts (e.g. surgeons). Theonline learning portion in block 18 (exemplarily illustrated inflowchart 200 in FIG. 6) can include an as-performed or sequentiallyupdated training of the ATP learning program 14 or saved portionthereof. It will be understood, therefore, that the online learningportion 18 can include acquiring data or inputting data while performingan actual procedure on a selected subject, but does not require asubject to be selected but an rather include simply additional trainingafter the offline learning in block 16.

Once the ATP learning program 14 is implemented or trained, adetermination of whether a procedure is to be performed is made indecision block 20. If no procedure is to be performed, a NO path 22 canbe followed to an END block 24. However, if a procedure is to beperformed, the YES path 30 can be followed.

When a procedure is to be performed, the YES path 30 is followed toinput patient data in block 32. The patient data inputted in block 32can be any appropriate patient data including the patient data that isinputted for the offline learning in block 16. The offline learningprocedure in block 16 and the online learning procedure in block 18 willbe discussed in further detail as will the discussion of appropriate orselected patient data (e.g. diagnosis, gender, etc.). It will beunderstood that patient data inputted in block 32 need not be every typeof patient data that is used in the offline learning in block 16, butrather can be any appropriate, selected, or obtained patient data.

After the patient data is inputted in block 32, a trajectory plan can beillustrated in block 34. As also discussed further herein, thetrajectory plan can be illustrated as an icon superimposed on image dataof a selected subject including an entry point, instrument path, andtarget. Accordingly, once the patient data is inputted in block 32, theATP program 14 can output one or more selected trajectories and/orratings of one or more selected trajectories and the trajectories and/orthe ratings can be illustrated in block 34. The illustrated trajectoryin block 34 can then be used to perform a procedure in block 36.Alternatively, or in addition thereto, other trajectories can bedetermined and rated.

In performing the procedure in block 36, it will be understood that anyappropriate procedure can be performed based on the input patient data.For example, if the input patient data includes the location of a tumorthe performed procedure can include following a trajectory illustrate inblock 34 to remove the tumor. Alternatively, it will be understood thatthe procedure can include a placement of pedicle screws, placement ofdeep brain stimulation (DBS) probes, or other appropriate procedures.Once the procedure is performed in block 36, the ATP system can end inblock 24.

The procedure illustrated in FIG. 1, generally includes a broad overviewof the ATP program in block 14 and performing a procedure in block 36based thereon. It will be understood, however, that the ATP program 14can be used to perform a procedure after being implemented by a selecteduser, and the ATP program 14 can continue to learn from the user in theonline learning portion in block 18.

With reference to FIG. 2, an example of the procedure in flowchart 10 isillustrated as a clinical application procedure in flowchart 50.Generally, the procedure can start in START block 52. The procedure canthen proceed into the ATP 14, including to offline learning in block 54which is similar or identical to the offline learning 16 in FIG. 1. Theoffline learning, as discussed further herein, can include training alearning algorithm, including the ATP program 14, based upon inputs anddata from a plurality of training datasets from or based on selectedpatients and/or cases. For example, expert raters (e.g. selectedsurgeons, practitioners, etc.) can be selected to rate variousprocedures and the algorithm can learn how to rate further oradditionally inputted procedures. Once appropriate learning is achievedin the offline learning in block 54, an output of ATP initialinstructions can be performed in block 56. The output ATP initialinstructions in block 56 can generally be understood to be a softwarepackage that can be distributed for various users or clinicians.

After the output ATP initial instructions (also referred to as learnedcoefficients) 56 are loaded on selected systems, such as a computersystem or processor system (e.g. the processor system 500 in FIG. 9)that can include a selected processor for executing instructions, newsubject data can be accepted or input in block 58. The new subject dataaccepted in block 58 can be data that relates to any subject other thanthe subjects or at least the particular data inputted for the offlinelearning in block 54. It will be understood also that the continued oronline learning can be substantially user specific, therefore it can beselected to input a user I.D. in block 60 either prior to, consecutivelyor concurrently with new subject data, or after accepting new subjectdata. User specific capturing can be specific to a rating for a selectedtarget, metric (e.g. safety, efficacy, etc.), or other user specificselection.

After accepting new subject data, a plan can be output in block 62 basedupon the outputted ATP initial instructions in block 56. The outputtedplan can include a rating determined by the ATP initial instructionsfrom block 56 and can then be rated in block 64 by a clinical user orconsumer who was identified in block 60. Generally, the clinical user isa user using the ATP system 14 to plan or perform a procedure on aselected subject, as illustrated in FIG. 9. The rating can be anyappropriate rating by the user and can be based upon the user'sexperience, patient condition, or the like. The clinical user isseparate from the selected expert raters used in the offline learning inblock 54. The rating of the outputted plan can be used in an onlinelearning 66 (similar or identical to the online learning 18), which canbe used to further teach the ATP system 14. Accordingly, the ATPinstructions can be updated and output updated ATP instructions can bemade in block 68.

The outputted updated ATP instructions can be saved in block 70. Theoutputted saved ATP instructions can then be used to further analyze theaccepted new subject data in block 58. Also, the user I.D. can again bequeried to ensure that the same user that caused the outputted updatedATP instructions to be saved is also using the system. As discussedfurther herein, the online learning can assist in determining updatedATP instructions that are user specific, thus querying the user I.D. canensure that the appropriate instructions are used for the plan rating bythe ATP system 14.

A final plan can then be outputted in block 72 that is based upon thesaved updated ATP instructions and accepted by the clinical user. Thefinal plan from block 72 can also include a plan or trajectory that isconstrained by a final score against one or more metrics (e.g. efficacy,safety, etc.). The final plan may alternatively or also be constrainedby entrypoint or region defined by a user, or can include an output ofall acceptable plans in graphical or textual form. For example, asillustrated in FIG. 7, a plurality of plans may be illustrated ifselected by a user and the plurality of plans can be a graphical outputas the final plan.

Upon acceptance of a trajectory from the ATP system 14, a procedure canthen be performed in block 74 based upon the outputted final plan andthe procedure can end in block 76. Thus, the ATP learning system canthen be used to identify appropriate trajectories and can be alteredbased upon user input to further teach the algorithm to assist inidentifying or rating trajectories for the user. This can assist inproviding user specific trajectories and trajectories or ratings thatwill be more readily accepted by a particular user based upon theinstructions. Nevertheless, the algorithm can use the initial or offlinelearning to assist in analyzing and rating trajectories for the user inthe initial output plan in block 62 when used in a new or subsequentprocedure.

The ATP system can be a learning system as briefly discussed above, anddiscussed in further detail herein. The various learning can includeboth offline learning using original or initial data, as brieflydiscussed above, and also further discussed further herein. The data canbe analyzed and used to provide trajectories or suggest trajectoriesthat are rated by selected users to assist in providing a baseline orinitial ATP system that can be used to rate new trajectories.Accordingly, the offline learning can initially train the ATP system.The online learning, as also discussed further herein, can be used tocustomize and enhance the rating based on a specific clinical user. Forexample, a specific user may prefer or rate a certain type of plan (e.g.a straight as opposed to a curved trajectory) better or worse. Thus, theonline learning can assist in speeding in selecting a trajectoryacceptable to a particular condition and/or user.

With reference to FIG. 3, an offline learning process 100 isillustrated. The offline learning process 100 can include training analgorithm, as discussed further herein, to assist in performing ordetermining at least ratings (e.g. better or worse or more or lesspreferred) of trajectory plans. As discussed above, offline learning caninclude offline learning in block 16 or block 54, according to theselected procedure. As discussed above, generally, offline learningincludes selected expert raters that have rated plans in training datasets. Accordingly, offline learning can start in block 110. Regardless,the offline learning 100 is used to train a learning algorithm to rateplans including trajectories for selected procedures. Procedures caninclude surgical procedures, such as tumor removal or DBS placement.

The offline learning 100 can then input a selected quantity of datasetsin block 112. The datasets inputted in block 112 can include appropriatedata or various specific data points. For example, a dataset can includeimage data (including images of a subject, such as a patient 404, FIG.9) and information relating to a particular patient for a particularprocedure. As a specific example, performing a procedure can includemoving a deep brain stimulation (DBS) probe to a sub-thalamic nucleus(STN). The selected dataset, therefore, can include information relatingto the specific procedure on a patient and specific data of the patient(e.g. image data). For example, a single selected patient X can have adataset that can include various information relating to patient X. Thepatient and the single procedure relating to the patient X can includeimage data relating to the patient. In the example of placing a DBS in abrain that dataset can include image data of a brain. The image data caninclude magnetic resonance imaging (MRI) data, computed tomography (CT)data or other appropriate image data. The image data can be only aportion of the dataset. Additionally, data can include a plan, eitherpreplanned or performed and can include a trajectory of an instrumentfor placing the DBS within the brain of patient X. The plan can includean actual plan performed on the patient X or a plan for performing aprocedure on patient X. Accordingly, the plan can include identifyingvarious portions of the image data, such as an anterior commissure (AC)and a posterior commissure (PC) in the image data. Additionally, theplan can include an identified target point (e.g. a point within theSTN) and an entry point. The entry point can then be a point throughwhich the instrument will pass to enter into the brain of the patient.The entry point can be on the skin of the patient, the skull bone of thepatient, or any other appropriate entry point. The particular datasetcan also include specific information relating to the patient X. PatientX specific information can include demographic data such as an age ofpatient X, a gender of patient X, or other patient specific information.Patient specific information can further include previous proceduresthat have been performed on patient X, other conditions of patient X, orthe like. Additionally, datasets can include information relating to theclinician performing the procedure. For example, as discussed furtherherein, the ATP can be specialized for a particular clinician.Accordingly, an I.D. of a surgeon or clinician can be entered as a partof the dataset.

A plurality or selected quantity of training datasets can be acquiredfor input in block 112. The training datasets can be from a selectedpopulation or sample thereof (e.g. selected number of prior patients orselected patients) to train the ATP system. For each of the trainingdatasets an input of a rating for the plan can be inputted by a selectedrater in block 114. For example, in the initial offline learningprocedure, selected practitioners or expert raters can be selected torate the training dataset that include plans. Each dataset includes aplan, either one based upon a preplanned procedure or a plan based upona performed procedure, and the plan can be rated by an expert.

The rating can be any appropriate rating system, in block 114, forexample a rating of 1-10 where a rating of 10 can be a plan that isidentified as substantially perfect by the rating individual or 1 refersto a plan that includes extreme hazard or is substantially undesirableby the selected rater. Accordingly, the selected expert or practitioner(herein also referred to as a rater) can rate in block 114 each of theplans from the input dataset from block 112. It is understood that anyappropriate coding can be used to rate at least plans or trajectoriesused in the training datasets.

The particular rater can rate every dataset input in block 112 toprovide a complete set of ratings based upon the individual rater. Aplurality of plans or trajectories can be rated for each dataset. Eachdataset, however, can be specific to a particular plan where aparticular plan can include a single trajectory having an entry pointand a target within a selected region that can be rated by the rater.Accordingly, a plurality of datasets can be rated by the raters in block114 to provide a plurality of ratings regarding a plurality of datasetsthat are input in block 112 to provide information of ratings of aplurality of trajectories based on a plurality of input datasets.

Additionally, the input data for the different datasets can be varied ordegraded to account for later variances in input data quality (e.g. 1tesla or two tesla MRI) or type (e.g. CT scan or MRI scan). Datasetsincluding different plans can be input with different types of imagingmodalities or different qualities of image data input, as illustrated inFIG. 4. For example, a training dataset including a trajectory plan(wherein the trajectory plan can include an entry point 180, a target230, and a path 182) can be input using MRI data 220 that was obtainedwith a one tesla MRI system. A substantially identical entry point,trajectory, and target for the same patient can also be input with imagedata acquired with a five tesla MRI system. Accordingly, a user can ratethe substantially identical trajectory plan using image data both theone tesla MRI system and the five tesla MRI system to provide ratingsfor different qualities of image data to be used in the offlinelearning. It is understood that the rating can also include a pluralityof rating metrics or measures. Exemplary rating metrics can includesafety, efficacy side effect to a patient of the plan, expected surgicalcomplexity and/or time, risk of damage to select anatomical orfunctional areas, and other appropriate or selected metrics. Each expertrater can rate each plan according to any and/or all of the selectedmetrics. Thus, each rated plan can include a combination of ratings foreach of the metrics. Similarly, each plan that is identified or testedby the system can then be rated according to each of the metrics. Thus,an output can include a blended or composite rating (e.g. 8.2/10overall) or a plurality of ratings where each rating is for one metric(e.g. safety=9.5/10; efficacy=3/10). Also, sub-sets of the metrics couldbe blended, such as a combination of safety and damage to selected areascan be blended while efficacy is left separate.

Once the different plans are rated, or after ratings and plans have beencorrelated or associated, the various trajectory plans (generallyincluding the path, entry point, and target point) can be analyzed.Analysis of the plans can include various features as discussed herein.For example, shell modeling can be used to sample the image data alongthe trajectory in the selected dataset in block 116.

In shell sample data of the trajectories, the image data around thetrajectory can be sampled by identifying a shell sample of trajectoriessubstantially parallel to the trajectory in the selected dataset.Accordingly, it will be understood that sampling the image data in ashell model can be performed in each of the selected quantity ofdatasets inputted in block 112. In particular, it will be understoodthat the image data can be processed to identify various features of thetrajectory in each of the selected datasets. Generally, each of theimage data can be preprocessed using a median or selected low passkernel. The image data can also be formatted and coordinated to alignthe image data along or with selected coordinates. For example, the ACand PC can be used to align the data within the selected datasets. Ashell image can then be computed along the trajectory of the plan in theselected dataset.

In generating the shell image, the image data can be sampled along thetrajectory line between the target and the entry point within the imagedata. A shell sample can then be determined. As discussed briefly above,the shell sample can include determining a plurality of pathssubstantially parallel to the selected or determined trajectory withinthe selected dataset. As illustrated in FIG. 5, a shell sample caninclude a selected trajectory (e.g. an initial or input plannedtrajectory) that can be identified as a center trajectory 120. Aselected plurality of other shell trajectories, such as a first shelltrajectory 122 and a second shell trajectory 124, can be identifiedparallel to the first and generally pass through the image data atdifferent points than the first trajectory 120. The shell sampling, byidentifying a plurality of trajectories substantially parallel to theinitial or planned trajectory 120, allows sampling the image data alongtrajectories other than the single trajectory of the plan to allow forsampling of data within the image data to create a greater sample thanthe single trajectory line. Further, it allows sampling a selectedportion of the dataset substantially equivalent relative to the selectedplanned trajectory.

Once the shell sample has been determined, the intersection of the skinor other appropriate anatomical portion, such as the skull, can bedetermined within the image data using generally known edge or surfacedetection algorithms such as Sobel, Canny, or Zero-crossing. Detectioncan be based upon contrast or other appropriate edge detection todetermine the skin surface or bone surface in the shell sample data. Theimage data can then be aligned based upon the determination of the skinor entry surface. The image data can then be sampled along the selectednumber of trajectories or shell trajectories.

Once the image data has been sampled into the shell data sample, theimage data or the data relating to the shell sample can be normalizedand/or compressed in block 130. Generally, it can be selected tonormalize or compress the features of the sample data to minimizecomputational stresses and better correlate the variables that definethe trajectories within the sample. Specific normalization orcompression routines can include principal component analysis (PCA),independent component analysis (ICA), or two-dimensional discreet cosigntransforms (2D-DCT). The various normalization or compression techniquescan be used singly or in combination to compress or normalize the shellsample data for further analysis and use in learning algorithm. Inparticular, the dimensionality of the data can be reduced by performingselected transforms, such as the PCA. The normalized or compressed datacan then be stored or used, either immediately or in furthercalculations, as discussed herein.

Geometric features of the trajectory can be measured or determined inblock 140. The geometric configurations or features can be based uponthe compressed or normalized data saved in block 130 or on the rawsample data obtained or determined in block 116. Nevertheless, geometricfeatures can include the length of the trajectory or line from the entrypoint to the target point, angle calculations relative to the skinsurface, planes of the anatomy, and geometric relations to known oridentified structures. For example, structures can be identified in theimage data (e.g. vasculature, commissure, sulci).

The identification of the structures can be based on automaticidentification or by user identification. Nevertheless, the structurescan be identified as either “good” or “bad”. Good structures can includethose structures that are preferred to be intersected with a selectedtrajectory. Bad structures can include those that are preferred to notbe intersected or are selected to never be intersected with atrajectory. Accordingly, each of the structures identified in the imagedata can be weighted for use in the learning algorithm discussed furtherherein. In addition, the plan length can be used to identify a selectedweight for use in the analysis of the learning algorithm as well. Theangle or geometry of the trajectory can be calculated relative to theidentified structures.

After the image data has been analyzed and sampled by a shell samplemodel in block 116, compressing or normalizing data in block 130 andmeasuring various geometric features of the trajectory in block 140,coefficients can be determined in block 150. The coefficients can anyappropriate type of coefficients for use in the learning algorithm or asa part of the learning algorithm as discussed further herein.

The coefficients determined in block 150 can be a coefficient that isbased upon various learning techniques such as linear regression,neural-network systems, genetic programming systems or other appropriatealgorithm systems. Generally the learning algorithm can determine thecoefficients that can be used to determined a rating for a selectedtrajectory. The different coefficients can be determined by achievingconvergence or measuring convergence of the selected coefficienttrajectory plans relative to the rated plans. The convergence can bebased upon an absolute difference between the rating of the trajectoriesand the determined trajectories or a root mean square error between thedetermined plans or the correct or rated plans. The determinedcoefficients (i.e. learned coefficients) may then be used to rate plansbased on different datasets (e.g. current subject datasets based on asubject for a current procedure to be performed).

A generated trajectory can be rated with the learned coefficients basedupon the plurality of rated datasets originally input in block 112.Obtaining convergence of the predicted ratings allows for determiningthe learned or determined coefficients between the selected trajectoriesand the rated trajectories input in block 112. The learned or determinedcoefficient in block 150 can be saved in block 160. The savedcoefficients can then be stored for later trajectory determination orrating based upon later input data, as discussed herein. The offlinelearning can end in block 162.

It will be understand, however, that the saved coefficients can be usedto assist in rating or determining trajectories in a new or selectedpatient or operative patient dataset. Accordingly, with continuingreference to FIG. 3 and additional reference to FIG. 6, the savedcoefficients can be used to rate trajectories according to a ratingsystem 200. The rating system 200 can begin in block 202 after whichoperative patient data is input in block 204. A surgeon ID canoptionally be put in block 206, as discussed above the learningalgorithm can be specified for a particular user based upon the inputsurgeon ID in the learning algorithm. Accordingly, the saved coefficientinputs in block 160 can be correlated or determined for a particularsurgeon (e.g. clinical user as discussed above), which can be recalledbased upon the input of the surgeon ID in block 206.

After the optional input of the surgeon ID in block 206, a selectedentry region or plurality of trajectories can be input in block 208. Asillustrated in FIG. 7, an image or slice of a volume of the image 220 isillustrated. The image 220 can be image data of the selected patientinput in block 204. As exemplary illustrated four entry points 222 a-222d are illustrated as is a target 230. Additionally, four paths 232 a-232d extend from each of the entry points 222 a-222 d to the target 230.Each of the entry points 222 a-222 d and the paths 232 a-232 d can beidentified by the surgeon whose ID is input in block 206. Alternatively,the entry points 222 a-222 d in the respective paths 232 a-232 d can begenerated automatically with a planning system or identified by anotheruser. Regardless, a plurality of entry points and paths can beidentified. Alternatively, or in additional thereto, a region (e.g. asurface area at an exterior of the patient) can be identified and one ormore points within the region can be determined to be an entry point andtrajectories can be determined from the entry point in the region to thetarget 230 by the system. Exemplary methods or systems for determiningregions and possible trajectories from a point in a region to a targetare can include those disclosed in U.S. patent application Ser. No.12/103,488, published on Oct. 15, 2009 as U.S. Pat. App. Pub.2009/0259230; Ser. No. 11/584,813, published on May 29, 2008 as U.S.Pat. App. Pub. 2008/0123922; Ser. No. 11/683,796, published on Apr. 3,2008 as U.S. Pat. App. Pub. 2008/0081982; and Ser. No. 11/683,695,published on Mar. 13, 2008 as U.S. Pat. App. Pub. 2008/0064947; allincorporated herein by reference. In brief, a surface area or arearelative to a patient can be identified as an entry region and aselected number or all possible trajectories from within the identifiedsurface area to the target can be identified and analyzed.

Once the trajectories or regions are identified in block 208, adetermination of patient specific features in the data input of theoperative patient in block 204 can be performed in block 240. Thedetermination of patient specific features can be similar to thesampling or image processing discussed in relation to the planningalgorithm above. For example, the demographic or patient specificinformation (e.g. gender, age) can be identified or included in the datainput. Additionally, the geometric features of the input trajectories ordetermined trajectories from the region can be determined. Also,specific image trajectory features can be identified, such as positionrelative to cautious zones (e.g. sulci) or selected passage zones can beidentified. The data input in block 204 and the determination of patientspecific features can then be normalized or compressed block 244.

The normalization or compression of the input data can include samplingwithin the shell around the selected or input trajectories, decreasingdimensionality of the data using various analyses (e.g. PCA) or otherappropriate normalization or compression algorithms. Generally, it isselected to normalize the input data to substantially match the dataused during the teaching of the learning algorithm as discussed inrelation to the process illustrated in FIG. 3.

Once the data has been normalized or compressed in block 244, the savedcoefficients can be recalled in block 246. The recalled coefficients canbe the coefficients saved in block 160 illustrated in FIG. 3. Becausethe coefficients relate to correlation between the rated plans as inputin the input dataset and in data that is substantially normalized orcompressed similar to the normalizing in block 244, the inputtrajectories from block 208 can be rated based upon the recalledcoefficients in block 246. Accordingly rating the input trajectorieswith the recalled coefficients can occur in block 250. The rating of theinput trajectories can rate the input trajectories in block 208 or allof the trajectories from the specific region identified in block 208.Based upon the coefficients, ratings can be applied to the trajectoriesand ratings for the trajectories can be output in block 252. The outputof ratings can be any appropriate output in block 252.

The output of the ratings can include a visual display, such asillustrated in block 252, that includes a chart or table 260 thatillustrates ratings similar to ratings used in the learning discussedabove. Alternatively, or in addition thereto, the rating number or valuecan be illustrated near to the trajectory lines 232 a-232 d. It willalso be understood that other indicia including coloring of the lines232 a-232 d based upon the rating determinations or other appropriateindicia. Alternately, or in addition thereto, only the highest ratedtrajectory can continue to be displayed or displayed on a display 502(FIG. 9). Accordingly, the output rating of the trajectories in block252 can include the display 502 illustrating only the image data 220 andthe highest ranked trajectory or highest rated trajectory. The use orapplication of coefficient can end in block 280. Accordingly, the outputrating of the trajectories in block 252 can be used by the surgeon toindentify or determine the ratings of the selected trajectories prior toperforming a procedure. It will also be understood that the selection orrating can be done substantially intraoperatively to assist indetermining an appropriate trajectory for performing a procedure. Asdiscussed above and discussed further herein, placement of a DBS probecan be positioned within the brain of the patient 404 (FIG. 9) along aselected trajectory. By rating the trajectories in a substantiallynon-subjective manner with the learned coefficients (wherein the learnedcoefficients are based upon predetermined input regarding a plurality ofsimilar procedures) the output of the rating of the trajectories inblock 252 can include rating or input based upon a plurality of selectedexperts in addition to the individual clinician performing the procedurebeing presently planned. The output or the ratings can then be used toassist in performing a specific and/or current procedure, which caninclude navigating a DBS probe to a selected target 230, including theSTN.

With reference to FIG. 8, an online or in-clinic learning or updatingsystem 300 can also be provided, for example as discussed above onlinelearning 18, 66. As discussed above, and illustrated in FIG. 1, bothonline and offline learning can be provided to determine thecoefficients of the learning algorithm. Accordingly, offline learningcan occur prior to or at any selected time to generate at least aninitial set of learned coefficients. The learned coefficients can bebased upon a selected set or number of datasets that can be updated oncea system is provided or installed at a selected location. Accordingly,online learning can be understood to be updates or learning that occursand updates the coefficients based upon a specific users input oradditional training inputs. Accordingly, an example of online learningcan include learning that occurs after a specific user obtains thesystem with the offline learned coefficients and provides additionalinputs to the system to update the coefficients. Accordingly, the onlinemethod 300 can be used to update the coefficients used to rate the inputplans or to define plans based on the input patient data.

The online learning 300 can start at block 302. A trajectory with imagedata can be presented in block 304. The presented trajectory can includea presentation such as that illustrated in FIG. 4 that illustrates theimage data 220, with a training trajectory including the entry point180, the target 230 and the training path 182. The training trajectorycan be selected based upon an operative patient or any other patient orsubject data. For example, the image data 220 can be a test case orhistorical data used for updating the coefficients. Nevertheless, oncethe trajectory is presented in block 304, the specific surgeon can ratethe trajectory in block 306. Rating the trajectory can be a rating onthe scale used the experts in the offline learning procedure.Accordingly, the online learning surgeon can rate the trajectory on thesame scale as used by the selected expert raters when training the ATPsystem 14 during the offline learning procedure.

Once the trajectory is rated in block 306, the trajectory and image datacan be sampled and processed similar to the offline learning.Accordingly, geometric considerations, placement, and other features inthe imaged data, along with PCA scores or factors can be used to analyzethe image data and the trajectory in block 308. Once the image data andthe trajectory is sampled to be similar to the image data used andsampling of the trajectories in the offline learning procedure, therating can be substantially correlated to the ratings of the offlineprocedure and the trajectory and the rating can then be used to updatethe coefficients in block 310. In updating the coefficients in block310, the saved coefficients can be updated based upon the additionalinput by the surgeon by rating the trajectory in block 306. The updatedcoefficients can then be saved in block 312 and the online learningprocedure can end in block 314. Accordingly, the online learningprocedure allows the specific user or surgeon to provide specific inputsto the system to allow for customization of the coefficients to thespecific surgeon. For example, a surgeon may preferably select aspecific geometry or rate trajectories based upon positions relative toanatomical structures differently than other clinical experts.Accordingly, by providing the online learning and updating thecoefficients in block 310, based upon ratings by specific surgeons orclinicians in block 306, the online learning can assist in learningspecific coefficients for specific users or updating the coefficientsfrom the offline learning to assist in providing substantially efficientand time efficient ratings based upon a specific surgeons input.

Using the online learning system 300 can allow a specific surgeon toupdate or rate trajectories. The updated coefficients can then be usedby the specific surgeon, as suggested above, to rate a plurality oftrajectories identified by the specific surgeon. The ratings for thespecific trajectories can then be based more specifically on thespecific users, or surgeons' inputs and desires. Accordingly, the ratingsystem can be customized for a specific user and reduce the variance ordifference between the rating of the trajectories and the specific userspersonal experience and expertise when determining trajectories to beused in a specific procedure.

The ATP system that is trained with the learning algorithm, discussedabove, can be used as a rating system to be used in planning orassisting in performing a procedure. The performed procedure can beperformed with a navigation system 400, as illustrated in FIG. 9. TheATP system can be incorporated into instructions that are executed by aplanning processor system 500, discussed further herein, either as aseparate processor system or incorporated in a processor system, such asa navigation processor 456. In any case, an instrument 446 can includesa DBS probe and can be navigated for performing a procedure. Theselected procedure can be any appropriate procedure such as a DBSplacement within a brain of the patient 404. It will be understood,however, that various instruments can be navigated along a selected orrated trajectories for various procedures such as placement of pediclescrews, guiding drug delivery stents or catheters, or other appropriateprocedures. Regardless, and discussed further herein, the navigationsystem 400 can be used to assist in performing the procedure that israted and/or selected based upon the ATP system.

FIG. 9 is a diagram illustrating an overview of a navigation system 400that can be used for various procedures. The navigation system 400 canbe used to track the location of an item, such as an implant or aninstrument, and at least one imaging system 402 relative to a subject,such as a patient 404. It should be noted that the navigation system 400may be used to navigate any type of instrument, implant, or deliverysystem, including: guide wires, arthroscopic systems, ablationinstruments, stent placement, orthopedic implants, spinal implants, deepbrain stimulation (DBS) probes, etc. Non-human or non-surgicalprocedures may also use the navigation system 400 to track anon-surgical or non-human intervention of the instrument or imagingdevice. Moreover, the instruments may be used to navigate or map anyregion of the body. The navigation system 400 and the various trackeditems may be used in any appropriate procedure, such as one that isgenerally minimally invasive or an open procedure.

The navigation system 400 can interface with or integrally include animaging system 402 that is used to acquire pre-operative,intra-operative, or post-operative, or real-time image data of thepatient 404. It will be understood, however, that any appropriatesubject can be imaged and any appropriate procedure may be performedrelative to the subject. The navigation system 400 can be used to trackvarious tracking devices, as discussed herein, to determine locations ofthe patient 404. The tracked locations of the patient 404 can be used todetermine or select images for display to be used with the navigationsystem 400.

The imaging system 402 can comprise the O-Arm® imaging device sold byMedtronic Navigation, Inc. having a place of business in Louisville,Colo., USA. The second imaging device 402 includes imaging portions suchas a generally annular gantry housing 410 that encloses an imagecapturing portion 412. The image capturing portion 412 may include anx-ray source or emission portion 414 and an x-ray receiving or imagereceiving portion 416. The emission portion 414 and the image receivingportion 416 are generally spaced about 180 degrees from each other andmounted on a rotor (not illustrated) relative to a track 418 of theimage capturing portion 412. The image capturing portion 412 can beoperable to rotate 360 degrees during image acquisition. The imagecapturing portion 412 may rotate around a central point or axis,allowing image data of the patient 410 to be acquired from multipledirections or in multiple planes.

The imaging system 402 can include those disclosed in U.S. Pat. Nos.7,188,998; 7,108,421; 7,106,825; 7,001,045; and 6,940,941; all of whichare incorporated herein by reference. The imaging system 402 can,however, generally relate to any imaging system that is operable tocapture image data regarding the subject 404. The imaging system 402,for example, can include a C-arm fluoroscopic imaging system, magneticresonance imagers, and computer tomography imagers which can also beused to generate three-dimensional views of the patient 404.

The patient 404 can be fixed onto an operating table 422, but is notrequired to be fixed to the table 422. The table 422 can include aplurality of straps 424. The straps 424 can be secured around thepatient 404 to fix the patient 404 relative to the table 422. Variousapparatuses may be used to position the patient 422 in a static positionon the operating table 422. Examples of such patient positioning devicesare set forth in commonly assigned U.S. patent application Ser. No.10/405,068, published as U.S. Pat. App. Pub. No. 2004/0199072 on Oct. 7,2004, entitled “An Integrated Electromagnetic Navigation and PatientPositioning Device”, filed Apr. 1, 2003 which is hereby incorporated byreference. Other known apparatuses may include a Mayfield® clamp.

The navigation system 400 includes at least one tracking system. Thetracking system can include at least one localizer. In one example, thetracking system can include an EM localizer 430, such as the EMlocalizer disclosed in U.S. Pat. App. Pub. No. 2004/019907 incorporatedabove and/or U.S. Pat. No. 7,751,865, incorporated herein by reference.The tracking system can be used to track instruments relative to thepatient 404 or within a navigation space. The navigation system 400 canuse image data from the imaging system 402 and information from thetracking system to illustrate locations of the tracked instruments, asdiscussed herein. The tracking system can also include a plurality oftypes of tracking systems including an optical localizer 432 in additionto and/or in place of the EM localizer 430. When the EM localizer 430 isused, the EM localizer can communicates with or through an EM controller450. Communication with the EM controller can be wired or wireless.

The optical tracking localizer 432 and the EM localizer 430 can be usedtogether to track multiple instruments or used together to redundantlytrack the same instrument. Various tracking devices, including thosediscussed further herein, can be tracked and the information can be usedby the navigation system 400 to allow for an output system to output,such as a display device to display, a position of an item. Briefly,tracking devices, can include a patient or reference tracking device (totrack the patient 404) 440, an imaging device tracking device 442 (totrack the imaging device 402), and an instrument tracking device 444 (totrack an instrument 446). The tracking devices allow selected portionsof the operating theater to be tracked relative to one another with theappropriate tracking system, including the optical localizer 432 and/orthe EM localizer 430. The reference tracking device 440 can bepositioned within the patient 404 or on a surface or connected to abone, such as a skull 448 of the patient 404

It will be understood that any of the tracking devices 440, 442, 444 canbe optical or EM tracking devices, or both, depending upon the trackinglocalizer used to track the respective tracking devices. It will befurther understood that any appropriate tracking system can be used withthe navigation system 400. Alternative tracking systems can includeradar tracking systems, acoustic tracking systems, ultrasound trackingsystems, and the like. Each of the different tracking systems can havedifferent and separate tracking devices and localizers operable with therespective tracking modalities. Also, the different tracking modalitiescan be used simultaneously as long as they do not interfere with eachother (e.g. an opaque member blocks a camera view of the opticallocalizer 432).

An exemplarily EM tracking system can include the STEALTHSTATION® AXIEM™Navigation System, sold by Medtronic Navigation, Inc. having a place ofbusiness in Louisville, Colo. Exemplary tracking systems are alsodisclosed in U.S. Pat. No. 7,751,865, issued Jul. 6, 2010 and entitled“METHOD AND APPARATUS FOR SURGICAL NAVIGATION”; U.S. Pat. No. 5,913,820,titled “Position Location System,” issued Jun. 22, 1999 and U.S. Pat.No. 5,592,939, titled “Method and System for Navigating a CatheterProbe,” issued Jan. 14, 1997, all incorporated herein by reference.

Further, for EM tracking systems it may be necessary to provideshielding or distortion compensation systems to shield or compensate fordistortions in the EM field generated by the EM localizer 430. Exemplaryshielding systems include those in U.S. Pat. No. 7,797,032, issued onSep. 14, 2010 and U.S. Pat. No. 6,747,539, issued on Jun. 8, 2004;distortion compensation systems can include those disclosed in U.S.patent Ser. No. 10/649,214, filed on Jan. 9, 2004, published as U.S.Pat. App. Pub. No. 2004/0116803, all of which are incorporated herein byreference.

With an EM tracking system, the localizer 430 and the various trackingdevices can communicate through an EM controller 450. The EM controller450 can include various amplifiers, filters, electrical isolation, andother systems. The EM controller 450 can also control the coils of thelocalizer 430 to either emit or receive an EM field for tracking. Awireless communications channel, however, such as that disclosed in U.S.Pat. No. 6,474,341, entitled “Surgical Communication Power System,”issued Nov. 5, 2002, herein incorporated by reference, can be used asopposed to being coupled directly to the EM controller 450.

It will be understood that the tracking system may also be or includeany appropriate tracking system, including a STEALTHSTATION® TRIA®,TREON®, and/or S7™ Navigation System having an optical localizer,similar to the optical localizer 432, sold by Medtronic Navigation, Inc.having a place of business in Louisville, Colo. Further alternativetracking systems are disclosed in U.S. Pat. No. 5,983,126, to Wittkampfet al. titled “Catheter Location System and Method,” issued Nov. 9,1999, which is hereby incorporated by reference. Other tracking systemsinclude an acoustic, radiation, radar, etc. tracking or navigationsystems.

The navigation system 400 can include or communicate with an imageprocessing unit 454 that can be housed in a support housing or cart 452of the imaging system 402. The cart 452 can be connected to the gantry410. The navigation system 400 can also include a navigation processingsystem or unit 456 that can communicate or include a navigation memoryfrom which image data, instructions, surgical plans (includingtrajectories), and other information can be recalled. The navigationprocessing unit 456 can include a processor (e.g. a computer processor)that executes instructions to determine locations of the trackingdevices based on signals from the tracking devices. The navigationprocessing unit 456 can receive information, including image data, fromthe imaging system 402 and tracking information from the trackingsystems, including the respective tracking devices and/or the localizers430, 450. Image data can be displayed as an image 458 on a displaydevice 460 of a workstation or other computer system 462 (e.g. laptop,desktop, tablet computer which may have a central processor to act asthe navigation processing unit 456 by executing instructions). Thecomputer system 462 can also include the navigation memory system. Theworkstation 462 can include appropriate input devices, such as akeyboard 464. It will be understood that other appropriate input devicescan be included, such as a mouse, a foot pedal or the like which can beused separately or in combination. Also, all of the disclosed processingunits or systems can be a single processor (e.g. a single centralprocessing chip) that can execute different instructions to performdifferent tasks.

The image processing unit 454 can process image data from the imagingsystem 402. The image data from the image processor can then betransmitted to the navigation processor 456. It will be understood,however, that the imaging systems need not perform any image processingand the image data can be transmitted directly to the navigationprocessing unit 456. Accordingly, the navigation system 400 may includeor operate with a single or multiple processing centers or units thatcan access single or multiple memory systems based upon system design.

In various embodiments, the position of the patient 404 relative to theimaging system 402 can be determined by the navigation system 400 withthe patient tracking device 440 and the imaging system tracking device442 to assist in registration. Accordingly, the position of the patient404 relative to the imaging system 402 can be determined. Otherregistration techniques can also be used, including those generallyknown in the art to register a physical space defined relative to thepatient 404 to image space defined by the image 458 displayed on thedisplay device 460.

Manual or automatic registration can occur by matching fiducial pointsin image data with fiducial points on the patient 404. Registration ofimage space to patient space allows for the generation of a translationmap between the patient space and the image space. According to variousembodiments, registration can occur by determining points that aresubstantially identical in the image space and the patient space. Theidentical points can include anatomical fiducial points or implantedfiducial points. Exemplary registration techniques are disclosed in Ser.No. 12/400,273, filed on Mar. 9, 2009, now published as U.S. Pat. App.Pub. No. 2010/0228117, on Sep. 9, 2010, incorporated herein byreference.

Either provided with or operable to communicate with the navigationsystem is a planning system 500. The planning system 500 can include acomputer processor (e.g. a central processing unit) to executeinstructions based on the learning algorithm 14, 50. The learningalgorithm can include the learned coefficients that are based on thetraining by the input of the selected experts in the offline learning100 or the online learning 300.

The planning system 500 can be incorporated in a separate system, suchas a separate portable computer system, or included in the processorsystem 462 discussed above. Regardless of the format, the planningsystem 500 is operable to execute instructions that are saved on anaccessible memory to rate trajectories that are input or from within aregion input. Accordingly, image data and other data of the operativepatient 404 can be recalled or input into the planning system 500. Theplanning system 500, in executing the selected instructions of the ATPsystem 14, can rate the trajectories as in block 250 and then output theratings as in block 252. In other words, the input patient data can beanalyzed, as discussed above, and the learned coefficients can be usedto rate or determine plans for a procedure. The output ratings can thenbe displayed on a display device 502 of the planning system 500 or onthe display device 460.

The planning system 500 can also be used for the online training 300.The online training can be performed at a selected clinic and theplanning system 500 can be used to allow the surgeon 18 to inputpersonal or specific ratings. The surgeon 18 can input information orselections and ratings into the planning system 500 such as with akeyboard, computer mouse, etc.

The planning system 500, therefore, can be used to plan a procedureprior to bringing the patient 404 into the operative theater. Oncetrajectories are rated, the surgeon can select an appropriatetrajectory, which includes an entry point and a path to the target. Theimage data can include the selected trajectory or the trajectory can besaved relative to a coordinate system of the image data. The image dataand the related coordinates system can then be registered to the patient404, as discussed above, once the patient 404 is moved into theoperating theater. It will be understood, however, that the planning canoccur while the patient 404 is in the operating theater, such as duringoperative preparation (e.g. operation site preparation, cleaning, etc.)rather than prior to moving the patient 404 into the operating theater.The procedure can be navigated using the navigation system 400 and usingthe selected trajectory.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

What is claimed is:
 1. A method of planning a trajectory to perform aprocedure, comprising: selecting a plurality of training datasetsregarding a plurality of training subjects, wherein each of theplurality of training datasets includes at least training image data;selecting at least one training trajectory in each of the selectedplurality of training datasets; rating the at least one trainingtrajectory in at least a selected sub-plurality of training datasets ofthe plurality of training datasets; executing instructions with alearning processor to process the training image data in each of atleast the selected sub-plurality of training datasets of the pluralityof training datasets; executing instructions with the learning processorto determine a learned rating coefficient that converges to a selecteddegree to predict the rating of each of the selected plurality oftraining trajectories; saving the determined learned rating coefficient;selecting a clinical dataset different from the plurality of trainingdatasets including a clinical trajectory and a clinical image data;executing instructions with a clinical processor to process the clinicalimage data substantially similar to the instructions executed with thelearning processor to process the training image data; executinginstructions with the clinical processor to determine a clinical ratingcoefficient that converges to a selected degree to predict the rating ofthe clinical trajectory; determining a rating for the clinicaltrajectory based on the clinical rating coefficient.
 2. The method ofclaim 1, wherein executing instructions with a learning processor toprocess the training image data includes sampling the training imagedata relative to the at least one training trajectory.
 3. The method ofclaim 2, wherein executing instructions with a learning processor toprocess the training image data further includes reducing dimensionalityof the sampled image data.
 4. The method of claim 1, wherein executinginstructions with the learning processor to determine ratingcoefficients includes executing a selected neural network algorithm tolearn the rating of the selected at least one trajectory in each of theselected sub-plurality of the plurality of training datasets.
 5. Themethod of claim 1, further comprising: selecting learning raters to ratethe at least one training trajectory in at least the selectedsub-plurality of training dataset of the plurality of training datasets;selecting a clinical rater to determine the clinical rating for theclinical trajectory; updating the saved learned coefficients with thedetermined clinical rating coefficient; and saving the updated learnedcoefficient.
 6. The method of claim 5, further comprising: executinginstructions with the clinical processor to rate a current subjecttrajectory based on a current subject dataset different from theplurality of training datasets with the saved updated learnedcoefficient.
 7. A system to determine a trajectory to perform aprocedure, comprising: a display device; an input device operable toallow an expert rater to input a rating of a trajectory; a learningprocessor operable to execute instructions to: display a trainingtrajectory on the display device along with training image data; receivean input from the input device including a rating of the trainingtrajectory; process the training image data at least to sample the imagedata in a shell relative to the training trajectory; and determine acoefficient relating the input rating and the displayed trainingtrajectory; a memory device operable to store the saved coefficient; acurrent subject data input device operable to input current subject dataand a current subject trajectory; and a rating processor operable toaccess the memory device including the saved coefficient and rate thecurrent subject trajectory with the saved coefficient to determine aclinical rating coefficient that converges to a selected degree topredict the rating of the subject trajectory.
 8. The system of claim 7,further comprising: an imaging device operable to acquire the imagedata.
 9. The system of claim 7, wherein the learning processor and therating processor are the same processor.
 10. The system of claim 7,wherein process the training image data further includes reducing thedimensionality of the shell sampled data.
 11. The system of claim 10,further comprising: a current subject data input device operable toinput current subject data and a current subject trajectory differentfrom the training trajectory; a rating processor operable to access thememory device including the saved coefficient and execute instructionsto: process the current subject image data in a manner similar to theprocess training image data to generated current subject processed dataand subsequently rate the current subject trajectory with the savedcoefficient based on the processed current subject process data.
 12. Thesystem of claim 11, wherein determine the coefficient relating the inputrating and the displayed training trajectory includes executinginstructions for at least one of a linear regression, a neural-network,or a genetic programming system based at least on the input rating ofthe trajectory.
 13. A method of planning a trajectory to perform aprocedure, comprising: inputting a rating for at least one trainingtrajectory in at least a selected sub-plurality of training datasets ofa plurality of training datasets, wherein each of the plurality oftraining datasets includes at least training image data; executinginstructions with a first processor to process the training image datain each of the selected sub-plurality of training datasets at least bysampling within a shell the training image data relative to the at leastone training trajectory in the respective training dataset; executinginstructions with a second processor to determine rating coefficient topredict the rating of each of the training trajectories based on theprocessed training image data and the input rating at least bydetermining a convergence of a selected coefficient for a selectedtraining trajectory and the input rating for the selected trainingtrajectory; saving the determined rating coefficient; inputting acurrent subject dataset different from the plurality of trainingdatasets including at least one current subject trajectory; executinginstructions with the second processor to determine a current subjectrating for the at least one current subject trajectory based on thesaved coefficients such that the current subject rating converges to aselected degree to predict the rating of the at least one currentsubject trajectory; and outputting the current subject rating.
 14. Themethod of claim 13, wherein executing instructions with the firstprocessor to process the training image data further includes decreasingdimensionality of the data sampled in the shell.
 15. The method of claim14, wherein decreasing dimensionality of the data sampled in the shellincludes projecting the shell sampled data into Principle ComponentAnalysis space.
 16. The method of claim 15, further comprising:selecting a rating surgeon; and selecting a rating scale; whereininputting the rating includes the selected rating expert rating the atleast one training trajectory based on the rating scale.
 17. The methodof claim 16, wherein selecting the rating surgeon includes selecting therating surgeon to analyze the plurality of training datasets and atleast one training trajectory in each training dataset of the pluralityof training datasets based on prior knowledge of the selected ratingsurgeon.
 18. The method of claim 16, further comprising: processing thecurrent subject dataset according to the processing of the trainingimage datasets.
 19. The method of claim 16, wherein the at least onecurrent subject trajectory includes a plurality of current subjecttrajectories; wherein executing instructions with the second processorto determine the current subject rating includes rating each currentsubject trajectory of the plurality of the current subject trajectories.20. The method of claim 19, wherein the plurality of current subjecttrajectories are generated as at least a portion of all of the possibletrajectories having an entry point with a selected area to reach aselected target.
 21. The method of claim 16, wherein the plurality oftraining datasets regarding the plurality of subjects is acquired priorto the current subject dataset and are different from the currentsubject dataset; wherein saving the determined rating coefficientsincludes saving the determined rating coefficients with a storagemedium; wherein executing instructions incorporating the savedcoefficients to rate the at least one current subject trajectoryincludes recalling the determined rating coefficients.
 22. The method ofclaim 13, wherein the first processor and the second processor are thesame processor.